Combined Features and Kernel Design for Noise Robust Phoneme Classification Using Support Vector Machines

• Published in: IEEE transactions on audio, speech, and language processing speech, and language processing, 2011-07, Vol.19 (5), p.1396-1407
• Main Authors: Yousafzai, Jibran, Sollich, Peter, Cvetkovic, Zoran, Bin Yu
• Format: Article
• Language: English
• Subjects: Acoustic noise; Acoustic waveforms; Acoustics; Applied sciences; Cepstral analysis; Classification; Exact sciences and technology; Information, signal and communications theory; Kernel; kernels; Noise; Noise levels; phoneme classification; Representations; robustness; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Speech; Speech processing; Speech recognition; Support vector machines; support vector machines (SVMs); Telecommunications and information theory; Training; Waveforms
• ISSN: 1558-7916; 2329-9290; 1558-7924; 2329-9304
• DOI: 10.1109/TASL.2010.2090657

This paper proposes methods for combining cepstral and acoustic waveform representations for a front-end of support vector machine (SVM)-based speech recognition systems that are robust to additive noise. The key issue of kernel design and noise adaptation for the acoustic waveform representation is addressed first. Cepstral and acoustic waveform representations are then compared on a phoneme classification task. Experiments show that the cepstral features achieve very good performance in low noise conditions, but suffer severe performance degradation already at moderate noise levels. Classification in the acoustic waveform domain, on the other hand, is less accurate in low noise but exhibits a more robust behavior in high noise conditions. A combination of the cepstral and acoustic waveform representations achieves better classification performance than either of the individual representations over the entire range of noise levels tested, down to - 18-dB SNR.